Good quality, low bit rate video coding is required for such applications as teleconferencing over existing and future networks, including ISDN. An effective low bit rate coder should remove the redundancy due to spatial and temporal correlations along with the perceptually irrelevant components of an image sequence. One very effective coder for still image compression (described in U.S. patent application Ser. No. 07/350,435 by J. D. Johnston and R. J. Safranek, entitled Perceptually-Tuned Sub-band Image Coder, filed May 4, 1989, and in R. J. Safranek and J. D. Johnston, A Perceptually Tuned Sub-band Image Coder With Dependent Quantization and Post Quantization, Proc. ICASSP, 1989) which incorporates statistical as well as perceptual criteria into the coding strategy. However, good quality full motion video coding at low bit rates (e.g., 384 kbps or less) has remained an elusive problem.
Sub-band digital coding techniques are well-known in the art. See, e.g.. N. S. Jayant and P. Noll, Digital Coding of Waveforms: Principles and Applications to Speech and Video, Prentice Hall, 1984.
Sub-band coding techniques have been used for image coding as described in G. Karlsson and M. Vetterli, Three Dimensional Sub-band Coding of Video, Proc. IEEE ICASSP, 1988, 1100-1103. The techniques described there employ multi-dimensional filtering to generate spatial-temporal sub-bands using so-called quadrature mirror filters. These latter filters are described, e.g., in J. D. Johnston, A Filter Family Designed for Use in Quadrature Mirror Filter Bands, Proc. IEEE ICASSP, 1980, and in the Jayant and Noll book, supra, chapter 11.
Another technique for encoding images is described in D. Chen and A. C. Bovik, Fast Image Coding Using Simple Image Patterns, SPIE, vol. 1199, Visual Communications and Image Processing IV (1989), pp. 1462-1471. The techniques described in the Chen and Bovik paper use a small number of local patterns as subimages, the selection of such patterns being based on measured properties of biological vision systems and a viewing geometry model. The selection of patterns (subimages) to represent an image is not based on typical error criteria such as the minimum mean square error metric.
A generally useful coding technique used to reduce required bit-rates is known as vector quantization. See, e.g., Jayant and Noll, supra, chapter 9, and A. Gersho, On the Structure of Vector Quantization, IEEE Trans. Info. Theory, vol. IT-28, pp. 157-165, March, 1982. Such techniques compare an input sequence to be coded to "vectors" stored in an ordered list or codebook. When the best match (in accordance with some predetermined criterion) is found in the codebook, the index for that vector is selected to represent the input sequence. Generally, some training operation is employed to generate the codebook and to update it over time.